Sub-vocalization processes, in general, relate to the domain of silent speech (SSP) and synthetic telepathy and limited advancements to date have occurred both in invasive and non-invasive domains of SSP. SSP can be produced in a variety of ways: (I) by talking by moving the speech articulators of a person but without producing any audible sound where the signals may be captured using Electromyography (EMG) sensors placed around the neck and mouth; (II) by imagery speech where a person imagines the word to be produced and (III) by talking in the mind without moving any speech articulators and without making any audible sound (i.e., sub-vocalization).
Neural Computer Interfaces (NCI) with the brain are communication and/or control systems that allow real time interaction for SSP between the human brain and external devices, without the need for vocalization. Using electroencephalography (EEG), NCIs for connecting and monitoring motor cortex functions of the brain, SSP detections may be enabled with sufficient signal-to-noise ratios generated from neural activities of the signals of the brain than other past invasive techniques used in signal generation that in addition, only met with limited success. EEG NCIs may allow for reinforcing the learning process of persons while maintaining minimally an invasive set of characteristics. Thus speech-like NCIs based on patterns of EEG recordings, is feasible. Through machine learning algorithms and pattern recognition, NCIs may be able to translate brain activity, predict a user's intents and convert them into commands which control external devices.
Sub-vocalization applications can eliminate deficiencies found in speech recognition applications particularly in noisy environments. However, sub-vocalization applications have applicability in wide domain areas that are not applicable to speech recognition applications, particularly when communications are needed not to be revealed. For example, sub-vocalization applications rather than voice recognition application are suited for use in silent communications in crowded environments, for confidential communications between parties, for sharing private information in public spaces while maintaining privacy; for communicating with parties without providing notice of communicating or revealing the communications to other third parties, for transmitting classified communications between parties or government entities etc. In addition, current voice recognition systems also use noise cancelation to try to achieve high accuracy in speech recognition in a noisy environment; and to reduce the environmental variations which cause noise in the speech signal. However, the use of noise cancelation is relatively ineffective to combat high levels of environmental noise distortions as well as variations in the level themselves that occur.
Hence, it is desirable to address these inadequacies raised in speech recognition in the communications that occur in various domains of internal and external communications by inner voice sub-vocalization methods, systems and apparatuses and to improve an overall NCI performance to allow for improved accuracy of sub-vocalization speech communications. The present disclosure addresses at least this need.